Cyber-attack is one of the most challenging aspects of information technology. After the emergence of the Internet of Things, which is a vast network of sensors, technology started moving towards the Internet of Things (IoT), many IoT based devices interplay in most of the application wings like defence, healthcare, home automation etc., As the technology escalates, it gives an open platform for raiders to hack the network devices. Even though many traditional methods and Machine Learning algorithms are designed hot, still it “Have a Screw Loose” in detecting the cyber-attacks. To “Pull the Plug on” an effective “Intrusion Detection System (IDS)” is designed with “Deep Learning” technique. This research work elucidates the importance in detecting the cyber-attacks as “Anomaly” and “Normal”. Fast Region-Based Convolution Neural Network (Fast R-CNN), a deep convolution network is implemented to develop an efficient and adaptable IDS. After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. This algorithm uses “Regression” tactics, a statistical technique to predict the continuous target variable that correlates between the variables. To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. The brainwaves made us to hybridize Fast R-CNN and Gradient Boost Regression (GBR) which reduces the loss function, processing time and boosts the model’s performance. All the above said methods are trained and tested with NIDS dataset V.10 2017. Finally, the “Decision Making” model decides the best result by giving an alert message. Our proposed model attains a high accuracy of 99.5% in detecting the “Cyber Attacks”. The experiment results revealed that the effectiveness of our proposed model surpasses other deep neural network and machine learning techniques which have less accuracy.

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Over the past few years the development in the mobile industry and development of internet, network for all, 4G, 5G etc. enabled the ordinary people as well as the elite people to depend upon mobile networks for regular business developments, entertainment, medical and educational needs. Almost all areas of development depend on the so called improvement of the mobile network. As the advantages and flexibility increases, the consumers entering by new registration increase widely and service requirement of existing consumers increase massively. It is mandatory to provide high level of security and dual privacy protection to the users sharing the large set of information through the cloud. The massive crowd sensing is important for any kind of network security system to ensure the detection of any miscellaneous activity entering the network grid. The study is focused on gathering various literature evidences on demand for intrusion detection system, analyzing the pitfalls in current models and creating an idea that would be helpful for us to proceed further with the research on intrusion detection system implementations and innovating a novel methodology that improvise from the present system. The future enhancement and interpretations on solutions would be discussed too.